{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as scs\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data/adult.data')\n",
    "cols = ['workclass', 'sex', 'age', 'education_num', 'capital_gain',\n",
    "        'capital_loss', 'hours_per_week', 'label']\n",
    "data = data[cols]\n",
    "data['label_code'] = pd.Categorical(data.label).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据分为训练集和测试集\n",
    "train_set, test_set = train_test_split(data, test_size=0.2, random_state=2310)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans_feature(data, category):\n",
    "    '''\n",
    "    根据传入的分段区间，将每星期工作时间转换为定量变量\n",
    "\n",
    "    参数\n",
    "    ----\n",
    "    data : DataFrame，建模数据\n",
    "\n",
    "    category : list，分段区间\n",
    "    '''\n",
    "    labels = ['{0}-{1}'.format(category[i], category[i+1]) for i in range(len(category) - 1)]\n",
    "    data.loc[:, 'hours_per_week_group'] = pd.cut(\n",
    "        data['hours_per_week'], category, include_lowest=True, labels=labels)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_prediction(re, test_set, alpha=0.5):\n",
    "    '''\n",
    "    使用训练好的模型对测试数据做预测\n",
    "    '''\n",
    "    # 关闭pandas有关chain_assignment的警告\n",
    "    pd.options.mode.chained_assignment = None\n",
    "    # 计算事件发生的概率\n",
    "    data = test_set.copy()\n",
    "    data['prob'] = re.predict(data)\n",
    "    # 根据预测的概率，得出最终的预测\n",
    "    data['pred'] = data.apply(lambda x: 1 if x['prob'] > alpha else 0, axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _auc(re):\n",
    "    fpr, tpr, _ = metrics.roc_curve(re['label_code'], re['prob'])\n",
    "    auc = metrics.auc(fpr, tpr)\n",
    "    return fpr, tpr, auc\n",
    "\n",
    "\n",
    "def evaluation(new_re, base_re):\n",
    "    '''\n",
    "    展示将每星期工作时间离散化之后，模型效果的变化\n",
    "    '''\n",
    "    fpr, tpr, auc = _auc(new_re)\n",
    "    # 为在Matplotlib中显示中文，设置特殊字体\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.rcParams.update({'font.size': 13})\n",
    "    # 创建一个图形框\n",
    "    fig = plt.figure(figsize=(6, 6), dpi=100)\n",
    "    # 在图形框里只画一幅图\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_title('ROC曲线')\n",
    "    ax.set_xlabel('FPR')\n",
    "    ax.set_ylabel('TPR')\n",
    "    ax.plot([0, 1], [0, 1], 'r--')\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    ax.plot(fpr, tpr, 'k', label=f'转换后的ROC曲线; AUC = {auc:0.3f}')\n",
    "    # 绘制原模型的ROC曲线\n",
    "    fpr, tpr, auc = _auc(base_re)\n",
    "    ax.plot(fpr, tpr, 'b-.', label=f'直接使用的ROC曲线; AUC = {auc:0.3f}')\n",
    "    legend = plt.legend(shadow=True)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.426128\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             label_code   No. Observations:                26048\n",
      "Model:                          Logit   Df Residuals:                    26043\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Mon, 06 Nov 2023   Pseudo R-squ.:                  0.2276\n",
      "Time:                        22:27:30   Log-Likelihood:                -11100.\n",
      "converged:                       True   LL-Null:                       -14370.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         -6.4633      0.105    -61.684      0.000      -6.669      -6.258\n",
      "education_num      0.3204      0.008     42.331      0.000       0.306       0.335\n",
      "capital_gain       0.0003   1.07e-05     31.534      0.000       0.000       0.000\n",
      "capital_loss       0.0008   3.52e-05     21.762      0.000       0.001       0.001\n",
      "hours_per_week     0.0382      0.001     26.397      0.000       0.035       0.041\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "# 直接使用，基准模型\n",
    "formula = 'label_code ~ education_num + capital_gain + capital_loss + hours_per_week'\n",
    "model = sm.Logit.from_formula(formula, data=train_set)\n",
    "re = model.fit()\n",
    "base_res = make_prediction(re, test_set)\n",
    "print(re.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.423736\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             label_code   No. Observations:                26048\n",
      "Model:                          Logit   Df Residuals:                    26040\n",
      "Method:                           MLE   Df Model:                            7\n",
      "Date:                Mon, 06 Nov 2023   Pseudo R-squ.:                  0.2319\n",
      "Time:                        22:27:31   Log-Likelihood:                -11037.\n",
      "converged:                       True   LL-Null:                       -14370.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                            -6.1776      0.125    -49.284      0.000      -6.423      -5.932\n",
      "C(hours_per_week_group)[T.20-40]      1.1916      0.095     12.503      0.000       1.005       1.378\n",
      "C(hours_per_week_group)[T.40-60]      1.9972      0.097     20.613      0.000       1.807       2.187\n",
      "C(hours_per_week_group)[T.60-80]      1.9353      0.129     15.024      0.000       1.683       2.188\n",
      "C(hours_per_week_group)[T.80-100]     1.7762      0.210      8.440      0.000       1.364       2.189\n",
      "education_num                         0.3126      0.008     41.135      0.000       0.298       0.327\n",
      "capital_gain                          0.0003   1.07e-05     31.344      0.000       0.000       0.000\n",
      "capital_loss                          0.0008   3.54e-05     21.645      0.000       0.001       0.001\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# 将每星期工作时间平均划分成5份\n",
    "category5 = range(0, 105, 20)\n",
    "train_data = trans_feature(train_set, category5)\n",
    "test_data = trans_feature(test_set, category5)\n",
    "formula = 'label_code ~ education_num + capital_gain + capital_loss + C(hours_per_week_group)'\n",
    "model = sm.Logit.from_formula(formula, data=train_data)\n",
    "re = model.fit()\n",
    "category5_res = make_prediction(re, test_data)\n",
    "print(re.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示模型结果\n",
    "re = evaluation(category5_res, base_res)\n",
    "re.savefig('continous_var_cut_5.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.420426\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             label_code   No. Observations:                26048\n",
      "Model:                          Logit   Df Residuals:                    26035\n",
      "Method:                           MLE   Df Model:                           12\n",
      "Date:                Mon, 06 Nov 2023   Pseudo R-squ.:                  0.2379\n",
      "Time:                        22:27:31   Log-Likelihood:                -10951.\n",
      "converged:                       True   LL-Null:                       -14370.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                            -5.8862      0.181    -32.539      0.000      -6.241      -5.532\n",
      "C(hours_per_week_group)[T.10-20]     -0.4022      0.196     -2.057      0.040      -0.785      -0.019\n",
      "C(hours_per_week_group)[T.20-30]     -0.1443      0.187     -0.772      0.440      -0.511       0.222\n",
      "C(hours_per_week_group)[T.30-40]      1.0024      0.160      6.258      0.000       0.688       1.316\n",
      "C(hours_per_week_group)[T.40-50]      1.6946      0.162     10.459      0.000       1.377       2.012\n",
      "C(hours_per_week_group)[T.50-60]      1.7782      0.167     10.666      0.000       1.451       2.105\n",
      "C(hours_per_week_group)[T.60-70]      1.6794      0.192      8.766      0.000       1.304       2.055\n",
      "C(hours_per_week_group)[T.70-80]      1.6088      0.226      7.129      0.000       1.167       2.051\n",
      "C(hours_per_week_group)[T.80-90]      1.7718      0.311      5.698      0.000       1.162       2.381\n",
      "C(hours_per_week_group)[T.90-100]     1.2554      0.307      4.088      0.000       0.654       1.857\n",
      "education_num                         0.3116      0.008     40.867      0.000       0.297       0.326\n",
      "capital_gain                          0.0003   1.07e-05     31.059      0.000       0.000       0.000\n",
      "capital_loss                          0.0008   3.55e-05     21.359      0.000       0.001       0.001\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# 将每星期工作时间平均划分成10份\n",
    "category10 = range(0, 105, 10)\n",
    "train_data = trans_feature(train_set, category10)\n",
    "test_data = trans_feature(test_set, category10)\n",
    "formula = 'label_code ~ education_num + capital_gain + capital_loss + C(hours_per_week_group)'\n",
    "model = sm.Logit.from_formula(formula, data=train_data)\n",
    "re = model.fit()\n",
    "category10_res = make_prediction(re, test_data)\n",
    "print(re.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示模型结果\n",
    "re = evaluation(category10_res, base_res)\n",
    "re.savefig('continous_var_cut_10.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_category(data):\n",
    "    '''\n",
    "    基于卡方检验，得到每星期工作时间的最优分段\n",
    "    '''\n",
    "    interval = [data['hours_per_week'].min(), data['hours_per_week'].max()]\n",
    "    _category = do_divide(data, interval)\n",
    "    s = set()\n",
    "    for i in _category:\n",
    "        s = s.union(set(i))\n",
    "    category = list(s)\n",
    "    category.sort()\n",
    "    return category\n",
    "\n",
    "def do_divide(data, interval):\n",
    "    '''\n",
    "    使用贪心算法，得到最优的分段\n",
    "    '''\n",
    "    category = []\n",
    "    p_value, chi2, index = divide_data(data, interval[0], interval[1])\n",
    "    if chi2 < 15:\n",
    "        category.append(interval)\n",
    "    else:\n",
    "        category += do_divide(data, [interval[0], index])\n",
    "        category += do_divide(data, [index, interval[1]])\n",
    "    return category\n",
    "\n",
    "def divide_data(data, min_value, max_value):\n",
    "    '''\n",
    "    遍历所有可能的分段，返回卡方统计量最高的分段\n",
    "    '''\n",
    "    max_chi2 = 0\n",
    "    index = -1\n",
    "    max_p_value = 0\n",
    "    for i in range(min_value + 1, max_value):\n",
    "        category = pd.cut(data['hours_per_week'], [min_value, i, max_value],\n",
    "                          include_lowest=True)\n",
    "        cross = pd.crosstab(data['label'], category)\n",
    "        chi2, p_value, _, _ = scs.chi2_contingency(cross)\n",
    "        if chi2 > max_chi2:\n",
    "            max_p_value = p_value\n",
    "            max_chi2 = chi2\n",
    "            index = i\n",
    "    return max_p_value, max_chi2, index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.419160\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:             label_code   No. Observations:                26048\n",
      "Model:                          Logit   Df Residuals:                    26040\n",
      "Method:                           MLE   Df Model:                            7\n",
      "Date:                Mon, 06 Nov 2023   Pseudo R-squ.:                  0.2402\n",
      "Time:                        22:27:43   Log-Likelihood:                -10918.\n",
      "converged:                       True   LL-Null:                       -14370.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Intercept                           -6.1007      0.107    -57.082      0.000      -6.310      -5.891\n",
      "C(hours_per_week_group)[T.34-37]     0.7750      0.109      7.138      0.000       0.562       0.988\n",
      "C(hours_per_week_group)[T.37-41]     1.2699      0.070     18.230      0.000       1.133       1.406\n",
      "C(hours_per_week_group)[T.41-49]     1.7780      0.081     21.997      0.000       1.620       1.936\n",
      "C(hours_per_week_group)[T.49-99]     1.9936      0.073     27.312      0.000       1.851       2.137\n",
      "education_num                        0.3118      0.008     40.824      0.000       0.297       0.327\n",
      "capital_gain                         0.0003   1.07e-05     31.098      0.000       0.000       0.000\n",
      "capital_loss                         0.0008   3.55e-05     21.325      0.000       0.001       0.001\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# 基于卡方检验划分区间\n",
    "category_chi2 = get_category(train_set)\n",
    "train_data = trans_feature(train_set, category_chi2)\n",
    "test_data = trans_feature(test_set, category_chi2)\n",
    "formula = 'label_code ~ education_num + capital_gain + capital_loss + C(hours_per_week_group)'\n",
    "model = sm.Logit.from_formula(formula, data=train_data)\n",
    "re = model.fit()\n",
    "category_chi2_res = make_prediction(re, test_data)\n",
    "print(re.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示模型结果\n",
    "re = evaluation(category_chi2_res, base_res)\n",
    "re.savefig('continous_var_cut_chi2.png', dpi=200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
